#Fp16

I generate 512x768 images on a 4GB GTX 1650. About 150 seconds per image in fp32 with model offload....

Quantising a small model down from FP16 to Q4_K_M on my RTX 5070 Ti more than doubled throughput and...

The limit lives in the tensor cores. When they multiply FP16 or BF16 numbers and add up the results in higher-precision FP32, GeForce cards issue those instructions...

An increasingly common design pattern for autonomous vehicles (AVs), robotics, and spatial AI systems is bird’s-eye-view (BEV) perception.

Every mixed-precision kernel makes the same decision, and it is usually made by hand: pick a format,...

A calibrated TensorRT INT8 build of RF-DETR put 0 layers in INT8 on an RTX 5090. Measured INT8, FP16, CPU quantization and distillation side by side.

A calibrated TensorRT INT8 build of RF-DETR put 0 layers in INT8 on an RTX 5090. Measured INT8, FP16, CPU quantization and distillation side by side.

KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag. - huawei-csl/KVarN

author: mrclaw207 published: false Every parameter in a standard LLM is a 16-bit floating...

I'm trying to evaluate the model's accuracy when all internal operations are strictly limited to **FP16**. However, I noticed that the `selective_scan` CUDA kernel seems to use **FP32 accumulators** by default.…